Xuehua He, Shan Li, Rongping Cha and co-authors published a critical narrative review in Frontiers in Psychology on July 30, 2026, examining artificial intelligence dependency among college students as generative artificial intelligence and large language model applications reshape learning, problem-solving and socio-emotional routines. The review defines AI dependency as an emerging educational-psychological construct, not a formal clinical disorder, and says benefits such as efficiency and personalized support exist alongside concerns about overreliance.

The review synthesizes evidence from the past decade on how college students use GenAI and LLM applications, with attention to conceptual definitions, theoretical frameworks, prevalence and demographic variation, measurement tools, determinants, intervention strategies and research gaps. Across studies, an “ask-AI-first” pattern appears increasingly common, but the authors caution that frequent use alone should not be treated as maladaptive dependency.

The central concern identified in the review is not AI use itself but cognitive delegation, emotional reliance and reduced self-regulation linked to weaker independent engagement, academic-integrity risks and distress when AI is unavailable. The authors say assessment remains fragmented, although several emerging tools show promising psychometric properties (evidence that a test measures reliably).

The review describes determinants as multi-level, including individual vulnerabilities, academic pressure, contextual norms and technological affordances. It recommends augmentation over automation, stronger AI literacy and critical evaluation skills, assessment redesign that preserves cognitive engagement, and supportive interventions for at-risk students. Future research priorities include longitudinal, cross-cultural and multi-method designs to clarify developmental trajectories, maladaptation thresholds and evidence-based intervention pathways.

Xuehua He, Shan Li, Rongping Cha and co-authors published a critical narrative review in Frontiers in Psychology on July 30, 2026, examining artificial intelligence dependency among college students as generative artificial intelligence and large language model applications reshape learning, problem-solving and socio-emotional routines. The review defines AI dependency as an emerging educational-psychological construct, not a formal clinical disorder, and says benefits such as efficiency and personalized support exist alongside concerns about overreliance.

The review synthesizes evidence from the past decade on how college students use GenAI and LLM applications, with attention to conceptual definitions, theoretical frameworks, prevalence and demographic variation, measurement tools, determinants, intervention strategies and research gaps. Across studies, an “ask-AI-first” pattern appears increasingly common, but the authors caution that frequent use alone should not be treated as maladaptive dependency.

The central concern identified in the review is not AI use itself but cognitive delegation, emotional reliance and reduced self-regulation linked to weaker independent engagement, academic-integrity risks and distress when AI is unavailable. The authors say assessment remains fragmented, although several emerging tools show promising psychometric properties (evidence that a test measures reliably).

The review describes determinants as multi-level, including individual vulnerabilities, academic pressure, contextual norms and technological affordances. It recommends augmentation over automation, stronger AI literacy and critical evaluation skills, assessment redesign that preserves cognitive engagement, and supportive interventions for at-risk students. Future research priorities include longitudinal, cross-cultural and multi-method designs to clarify developmental trajectories, maladaptation thresholds and evidence-based intervention pathways.